Overview

Dataset statistics

Number of variables17
Number of observations420551
Missing cells0
Missing cells (%)0.0%
Duplicate rows327
Duplicate rows (%)0.1%
Total size in memory54.5 MiB
Average record size in memory136.0 B

Variable types

Categorical3
Numeric14

Alerts

Dataset has 327 (0.1%) duplicate rowsDuplicates
Date Time has a high cardinality: 420224 distinct values High cardinality
T (degC) is highly correlated with Tpot (K) and 8 other fieldsHigh correlation
Tpot (K) is highly correlated with T (degC) and 8 other fieldsHigh correlation
Tdew (degC) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rh (%) is highly correlated with T (degC) and 4 other fieldsHigh correlation
VPmax (mbar) is highly correlated with T (degC) and 8 other fieldsHigh correlation
VPact (mbar) is highly correlated with T (degC) and 6 other fieldsHigh correlation
VPdef (mbar) is highly correlated with T (degC) and 5 other fieldsHigh correlation
sh (g/kg) is highly correlated with T (degC) and 6 other fieldsHigh correlation
H2OC (mmol/mol) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rho (g/m**3) is highly correlated with T (degC) and 7 other fieldsHigh correlation
wv (m/s) is highly correlated with max. wv (m/s)High correlation
max. wv (m/s) is highly correlated with wv (m/s)High correlation
h_sat is highly correlated with rh (%) and 1 other fieldsHigh correlation
T (degC) is highly correlated with Tpot (K) and 8 other fieldsHigh correlation
Tpot (K) is highly correlated with T (degC) and 8 other fieldsHigh correlation
Tdew (degC) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rh (%) is highly correlated with T (degC) and 5 other fieldsHigh correlation
VPmax (mbar) is highly correlated with T (degC) and 8 other fieldsHigh correlation
VPact (mbar) is highly correlated with T (degC) and 6 other fieldsHigh correlation
VPdef (mbar) is highly correlated with T (degC) and 4 other fieldsHigh correlation
sh (g/kg) is highly correlated with T (degC) and 6 other fieldsHigh correlation
H2OC (mmol/mol) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rho (g/m**3) is highly correlated with T (degC) and 8 other fieldsHigh correlation
wv (m/s) is highly correlated with max. wv (m/s)High correlation
max. wv (m/s) is highly correlated with wv (m/s)High correlation
h_sat is highly correlated with rh (%)High correlation
T (degC) is highly correlated with Tpot (K) and 7 other fieldsHigh correlation
Tpot (K) is highly correlated with T (degC) and 7 other fieldsHigh correlation
Tdew (degC) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rh (%) is highly correlated with VPdef (mbar) and 1 other fieldsHigh correlation
VPmax (mbar) is highly correlated with T (degC) and 7 other fieldsHigh correlation
VPact (mbar) is highly correlated with T (degC) and 6 other fieldsHigh correlation
VPdef (mbar) is highly correlated with T (degC) and 5 other fieldsHigh correlation
sh (g/kg) is highly correlated with T (degC) and 6 other fieldsHigh correlation
H2OC (mmol/mol) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rho (g/m**3) is highly correlated with T (degC) and 7 other fieldsHigh correlation
wv (m/s) is highly correlated with max. wv (m/s)High correlation
max. wv (m/s) is highly correlated with wv (m/s)High correlation
h_sat is highly correlated with rh (%) and 1 other fieldsHigh correlation
p (mbar) is highly correlated with rho (g/m**3)High correlation
T (degC) is highly correlated with Tpot (K) and 8 other fieldsHigh correlation
Tpot (K) is highly correlated with T (degC) and 8 other fieldsHigh correlation
Tdew (degC) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rh (%) is highly correlated with T (degC) and 5 other fieldsHigh correlation
VPmax (mbar) is highly correlated with T (degC) and 8 other fieldsHigh correlation
VPact (mbar) is highly correlated with T (degC) and 6 other fieldsHigh correlation
VPdef (mbar) is highly correlated with T (degC) and 4 other fieldsHigh correlation
sh (g/kg) is highly correlated with T (degC) and 6 other fieldsHigh correlation
H2OC (mmol/mol) is highly correlated with T (degC) and 6 other fieldsHigh correlation
rho (g/m**3) is highly correlated with p (mbar) and 9 other fieldsHigh correlation
wind_angle is highly correlated with wind_dirHigh correlation
wind_dir is highly correlated with wind_angleHigh correlation
h_sat is highly correlated with rh (%)High correlation
wv (m/s) is highly skewed (γ1 = -152.7160913) Skewed
max. wv (m/s) is highly skewed (γ1 = -144.7488651) Skewed
Date Time is uniformly distributed Uniform

Reproduction

Analysis started2022-08-11 05:37:05.507063
Analysis finished2022-08-11 05:38:25.401438
Duration1 minute and 19.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Date Time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct420224
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
21.03.2014 12:50:00
 
2
01.07.2010 03:50:00
 
2
01.07.2010 04:10:00
 
2
01.07.2010 04:20:00
 
2
01.07.2010 04:30:00
 
2
Other values (420219)
420541 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters7990469
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419897 ?
Unique (%)99.8%

Sample

1st row01.01.2009 00:10:00
2nd row01.01.2009 00:20:00
3rd row01.01.2009 00:30:00
4th row01.01.2009 00:40:00
5th row01.01.2009 00:50:00

Common Values

ValueCountFrequency (%)
21.03.2014 12:50:002
 
< 0.1%
01.07.2010 03:50:002
 
< 0.1%
01.07.2010 04:10:002
 
< 0.1%
01.07.2010 04:20:002
 
< 0.1%
01.07.2010 04:30:002
 
< 0.1%
01.07.2010 04:40:002
 
< 0.1%
01.07.2010 04:50:002
 
< 0.1%
01.07.2010 05:00:002
 
< 0.1%
01.07.2010 05:10:002
 
< 0.1%
01.07.2010 05:20:002
 
< 0.1%
Other values (420214)420531
> 99.9%

Length

2022-08-11T10:08:25.511182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14:50:002922
 
0.3%
15:30:002922
 
0.3%
14:00:002922
 
0.3%
15:00:002922
 
0.3%
15:10:002922
 
0.3%
15:20:002922
 
0.3%
13:00:002922
 
0.3%
13:10:002922
 
0.3%
13:20:002922
 
0.3%
13:30:002922
 
0.3%
Other values (3055)811882
96.5%

Most occurring characters

ValueCountFrequency (%)
02600557
32.5%
11082312
13.5%
2912743
 
11.4%
.841102
 
10.5%
:841102
 
10.5%
420551
 
5.3%
3273325
 
3.4%
5234752
 
2.9%
4233781
 
2.9%
6163198
 
2.0%
Other values (3)387046
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5887714
73.7%
Other Punctuation1682204
 
21.1%
Space Separator420551
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02600557
44.2%
11082312
18.4%
2912743
 
15.5%
3273325
 
4.6%
5234752
 
4.0%
4233781
 
4.0%
6163198
 
2.8%
9162674
 
2.8%
7112228
 
1.9%
8112144
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.841102
50.0%
:841102
50.0%
Space Separator
ValueCountFrequency (%)
420551
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7990469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02600557
32.5%
11082312
13.5%
2912743
 
11.4%
.841102
 
10.5%
:841102
 
10.5%
420551
 
5.3%
3273325
 
3.4%
5234752
 
2.9%
4233781
 
2.9%
6163198
 
2.0%
Other values (3)387046
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII7990469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02600557
32.5%
11082312
13.5%
2912743
 
11.4%
.841102
 
10.5%
:841102
 
10.5%
420551
 
5.3%
3273325
 
3.4%
5234752
 
2.9%
4233781
 
2.9%
6163198
 
2.0%
Other values (3)387046
 
4.8%

p (mbar)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6117
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.2127761
Minimum913.6
Maximum1015.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:25.683265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum913.6
5-th percentile974.95
Q1984.2
median989.58
Q3994.72
95-th percentile1002.49
Maximum1015.35
Range101.75
Interquartile range (IQR)10.52

Descriptive statistics

Standard deviation8.358480697
Coefficient of variation (CV)0.008449628734
Kurtosis0.9335259394
Mean989.2127761
Median Absolute Deviation (MAD)5.25
Skewness-0.4070227933
Sum416014422.2
Variance69.86419956
MonotonicityNot monotonic
2022-08-11T10:08:25.844896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990.96469
 
0.1%
989.73453
 
0.1%
991.08447
 
0.1%
989.36427
 
0.1%
991.2415
 
0.1%
990.71413
 
0.1%
989.48409
 
0.1%
988.87405
 
0.1%
990.59401
 
0.1%
989.24399
 
0.1%
Other values (6107)416313
99.0%
ValueCountFrequency (%)
913.61
< 0.1%
914.11
< 0.1%
917.42
< 0.1%
918.31
< 0.1%
918.51
< 0.1%
942.431
< 0.1%
942.541
< 0.1%
942.581
< 0.1%
942.591
< 0.1%
942.621
< 0.1%
ValueCountFrequency (%)
1015.351
 
< 0.1%
1015.31
 
< 0.1%
1015.292
< 0.1%
1015.281
 
< 0.1%
1015.261
 
< 0.1%
1015.231
 
< 0.1%
1015.211
 
< 0.1%
1015.21
 
< 0.1%
1015.191
 
< 0.1%
1015.173
< 0.1%

T (degC)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5530
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.450147354
Minimum-23.01
Maximum37.28
Zeros109
Zeros (%)< 0.1%
Negative55837
Negative (%)13.3%
Memory size3.2 MiB
2022-08-11T10:08:25.996682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-23.01
5-th percentile-3.86
Q13.36
median9.42
Q315.47
95-th percentile23.15
Maximum37.28
Range60.29
Interquartile range (IQR)12.11

Descriptive statistics

Standard deviation8.42336521
Coefficient of variation (CV)0.8913474991
Kurtosis-0.2006583656
Mean9.450147354
Median Absolute Deviation (MAD)6.06
Skewness-0.01927228954
Sum3974268.92
Variance70.95308147
MonotonicityNot monotonic
2022-08-11T10:08:26.143252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.13245
 
0.1%
5.2238
 
0.1%
10.33238
 
0.1%
8.59238
 
0.1%
11.93237
 
0.1%
12235
 
0.1%
6.97234
 
0.1%
10.4234
 
0.1%
8.1232
 
0.1%
5.53231
 
0.1%
Other values (5520)418189
99.4%
ValueCountFrequency (%)
-23.011
< 0.1%
-22.911
< 0.1%
-22.761
< 0.1%
-22.641
< 0.1%
-22.631
< 0.1%
-22.552
< 0.1%
-22.541
< 0.1%
-22.51
< 0.1%
-22.492
< 0.1%
-22.471
< 0.1%
ValueCountFrequency (%)
37.281
 
< 0.1%
37.131
 
< 0.1%
37.11
 
< 0.1%
37.091
 
< 0.1%
37.011
 
< 0.1%
36.873
< 0.1%
36.861
 
< 0.1%
36.791
 
< 0.1%
36.771
 
< 0.1%
36.711
 
< 0.1%

Tpot (K)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5639
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.4927434
Minimum250.6
Maximum311.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:26.297095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum250.6
5-th percentile270.04
Q1277.43
median283.47
Q3289.53
95-th percentile297.26
Maximum311.34
Range60.74
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation8.504471391
Coefficient of variation (CV)0.02999890328
Kurtosis-0.1396719124
Mean283.4927434
Median Absolute Deviation (MAD)6.05
Skewness-0.04251219348
Sum119223156.8
Variance72.32603365
MonotonicityNot monotonic
2022-08-11T10:08:26.438261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
282.69223
 
0.1%
282.68222
 
0.1%
281.46220
 
0.1%
281.57216
 
0.1%
282.2215
 
0.1%
281.89213
 
0.1%
282.66213
 
0.1%
281.36213
 
0.1%
288.6211
 
0.1%
282.33209
 
< 0.1%
Other values (5629)418396
99.5%
ValueCountFrequency (%)
250.61
< 0.1%
250.711
< 0.1%
250.851
< 0.1%
250.981
< 0.1%
251.061
< 0.1%
251.091
< 0.1%
251.172
< 0.1%
251.181
< 0.1%
251.222
< 0.1%
251.231
< 0.1%
ValueCountFrequency (%)
311.341
< 0.1%
311.211
< 0.1%
311.191
< 0.1%
311.061
< 0.1%
311.041
< 0.1%
311.021
< 0.1%
310.981
< 0.1%
310.971
< 0.1%
310.811
< 0.1%
310.781
< 0.1%

Tdew (degC)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4343
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.955853844
Minimum-25.01
Maximum23.11
Zeros216
Zeros (%)0.1%
Negative99829
Negative (%)23.7%
Memory size3.2 MiB
2022-08-11T10:08:26.593208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-25.01
5-th percentile-6.45
Q10.24
median5.22
Q310.07
95-th percentile15.14
Maximum23.11
Range48.12
Interquartile range (IQR)9.83

Descriptive statistics

Standard deviation6.730674308
Coefficient of variation (CV)1.358126071
Kurtosis-0.01879836989
Mean4.955853844
Median Absolute Deviation (MAD)4.92
Skewness-0.377110038
Sum2084189.29
Variance45.30197664
MonotonicityNot monotonic
2022-08-11T10:08:26.742054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.32280
 
0.1%
8.13278
 
0.1%
8.42277
 
0.1%
8.47275
 
0.1%
8.09273
 
0.1%
8.46272
 
0.1%
9.38271
 
0.1%
8.27267
 
0.1%
8.3266
 
0.1%
9.22265
 
0.1%
Other values (4333)417827
99.4%
ValueCountFrequency (%)
-25.011
< 0.1%
-24.851
< 0.1%
-24.81
< 0.1%
-24.711
< 0.1%
-24.661
< 0.1%
-24.631
< 0.1%
-24.611
< 0.1%
-24.581
< 0.1%
-24.551
< 0.1%
-24.521
< 0.1%
ValueCountFrequency (%)
23.111
< 0.1%
23.071
< 0.1%
23.062
< 0.1%
22.942
< 0.1%
22.861
< 0.1%
22.831
< 0.1%
22.641
< 0.1%
22.41
< 0.1%
22.212
< 0.1%
22.22
< 0.1%

rh (%)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4805
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.0082594
Minimum12.95
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:27.034151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.95
5-th percentile44.105
Q165.21
median79.3
Q389.4
95-th percentile97.2
Maximum100
Range87.05
Interquartile range (IQR)24.19

Descriptive statistics

Standard deviation16.47617537
Coefficient of variation (CV)0.2167682236
Kurtosis-0.3744500774
Mean76.0082594
Median Absolute Deviation (MAD)11.5
Skewness-0.6721579136
Sum31965349.5
Variance271.4643549
MonotonicityNot monotonic
2022-08-11T10:08:27.180757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001748
 
0.4%
90.51227
 
0.3%
94.81179
 
0.3%
93.91174
 
0.3%
90.61174
 
0.3%
90.21174
 
0.3%
90.11170
 
0.3%
94.21170
 
0.3%
881167
 
0.3%
90.91161
 
0.3%
Other values (4795)408207
97.1%
ValueCountFrequency (%)
12.951
< 0.1%
13.061
< 0.1%
13.521
< 0.1%
13.561
< 0.1%
13.882
< 0.1%
14.131
< 0.1%
14.21
< 0.1%
14.31
< 0.1%
14.441
< 0.1%
15.871
< 0.1%
ValueCountFrequency (%)
1001748
0.4%
99.9396
 
0.1%
99.8371
 
0.1%
99.7442
 
0.1%
99.6529
 
0.1%
99.5550
 
0.1%
99.4611
 
0.1%
99.3621
 
0.1%
99.2722
0.2%
99.1768
0.2%

VPmax (mbar)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3658
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.57625054
Minimum0.95
Maximum63.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:27.333506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile4.59
Q17.78
median11.82
Q317.6
95-th percentile28.4
Maximum63.77
Range62.82
Interquartile range (IQR)9.82

Descriptive statistics

Standard deviation7.739020057
Coefficient of variation (CV)0.570041046
Kurtosis2.39706337
Mean13.57625054
Median Absolute Deviation (MAD)4.64
Skewness1.311042423
Sum5709505.74
Variance59.89243145
MonotonicityNot monotonic
2022-08-11T10:08:27.487037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.83420
 
0.1%
10.26408
 
0.1%
10.64406
 
0.1%
12.02402
 
0.1%
10.86397
 
0.1%
10.59394
 
0.1%
10.22390
 
0.1%
10.43390
 
0.1%
11.94389
 
0.1%
10.12388
 
0.1%
Other values (3648)416567
99.1%
ValueCountFrequency (%)
0.951
 
< 0.1%
0.961
 
< 0.1%
0.971
 
< 0.1%
0.982
 
< 0.1%
0.996
< 0.1%
16
< 0.1%
1.014
< 0.1%
1.024
< 0.1%
1.035
< 0.1%
1.053
< 0.1%
ValueCountFrequency (%)
63.771
 
< 0.1%
63.261
 
< 0.1%
63.151
 
< 0.1%
63.121
 
< 0.1%
62.851
 
< 0.1%
62.373
< 0.1%
62.331
 
< 0.1%
62.11
 
< 0.1%
62.031
 
< 0.1%
61.831
 
< 0.1%

VPact (mbar)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2438
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.533755906
Minimum0.79
Maximum28.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:27.664153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile3.76
Q16.21
median8.86
Q312.35
95-th percentile17.23
Maximum28.32
Range27.53
Interquartile range (IQR)6.14

Descriptive statistics

Standard deviation4.18416434
Coefficient of variation (CV)0.4388789037
Kurtosis-0.2497782362
Mean9.533755906
Median Absolute Deviation (MAD)2.94
Skewness0.5566068691
Sum4009430.58
Variance17.50723122
MonotonicityNot monotonic
2022-08-11T10:08:27.813303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.92560
 
0.1%
6.04540
 
0.1%
6.03538
 
0.1%
5.97536
 
0.1%
6.08536
 
0.1%
6534
 
0.1%
6.09534
 
0.1%
5.9532
 
0.1%
5.84531
 
0.1%
6.06530
 
0.1%
Other values (2428)415180
98.7%
ValueCountFrequency (%)
0.791
 
< 0.1%
0.81
 
< 0.1%
0.812
 
< 0.1%
0.824
< 0.1%
0.836
< 0.1%
0.849
< 0.1%
0.853
 
< 0.1%
0.863
 
< 0.1%
0.873
 
< 0.1%
0.881
 
< 0.1%
ValueCountFrequency (%)
28.321
< 0.1%
28.261
< 0.1%
28.251
< 0.1%
28.241
< 0.1%
28.042
< 0.1%
27.91
< 0.1%
27.861
< 0.1%
27.531
< 0.1%
27.141
< 0.1%
26.831
< 0.1%

VPdef (mbar)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3649
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.042411574
Minimum0
Maximum46.01
Zeros1749
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:27.965597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21
Q10.87
median2.19
Q35.3
95-th percentile14.32
Maximum46.01
Range46.01
Interquartile range (IQR)4.43

Descriptive statistics

Standard deviation4.896850891
Coefficient of variation (CV)1.211368709
Kurtosis7.359157071
Mean4.042411574
Median Absolute Deviation (MAD)1.63
Skewness2.365400064
Sum1700040.23
Variance23.97914865
MonotonicityNot monotonic
2022-08-11T10:08:28.110818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01749
 
0.4%
0.271477
 
0.4%
0.311465
 
0.3%
0.31460
 
0.3%
0.341444
 
0.3%
0.291435
 
0.3%
0.321424
 
0.3%
0.241407
 
0.3%
0.281406
 
0.3%
0.221396
 
0.3%
Other values (3639)405888
96.5%
ValueCountFrequency (%)
01749
0.4%
0.01588
 
0.1%
0.02536
 
0.1%
0.03642
 
0.2%
0.04742
0.2%
0.05772
0.2%
0.06950
0.2%
0.071047
0.2%
0.081078
0.3%
0.09974
0.2%
ValueCountFrequency (%)
46.011
< 0.1%
45.531
< 0.1%
45.421
< 0.1%
45.412
< 0.1%
45.21
< 0.1%
44.871
< 0.1%
44.831
< 0.1%
44.81
< 0.1%
44.761
< 0.1%
44.521
< 0.1%

sh (g/kg)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1600
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.022408293
Minimum0.5
Maximum18.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:28.266697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.37
Q13.92
median5.59
Q37.8
95-th percentile10.92
Maximum18.13
Range17.63
Interquartile range (IQR)3.88

Descriptive statistics

Standard deviation2.656139026
Coefficient of variation (CV)0.4410426687
Kurtosis-0.2236757608
Mean6.022408293
Median Absolute Deviation (MAD)1.86
Skewness0.5679051857
Sum2532729.83
Variance7.055074524
MonotonicityNot monotonic
2022-08-11T10:08:28.420855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.79870
 
0.2%
3.8861
 
0.2%
3.78849
 
0.2%
3.77848
 
0.2%
3.81839
 
0.2%
3.85828
 
0.2%
3.74826
 
0.2%
3.92808
 
0.2%
3.69806
 
0.2%
3.82802
 
0.2%
Other values (1590)412214
98.0%
ValueCountFrequency (%)
0.52
 
< 0.1%
0.514
 
< 0.1%
0.5210
< 0.1%
0.5310
< 0.1%
0.544
 
< 0.1%
0.553
 
< 0.1%
0.563
 
< 0.1%
0.579
< 0.1%
0.5820
< 0.1%
0.5911
< 0.1%
ValueCountFrequency (%)
18.131
< 0.1%
18.091
< 0.1%
18.072
< 0.1%
17.941
< 0.1%
17.931
< 0.1%
17.851
< 0.1%
17.821
< 0.1%
17.611
< 0.1%
17.361
< 0.1%
17.142
< 0.1%

H2OC (mmol/mol)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2483
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.640223112
Minimum0.8
Maximum28.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:28.583208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3.8
Q16.29
median8.96
Q312.49
95-th percentile17.44
Maximum28.82
Range28.02
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation4.235394815
Coefficient of variation (CV)0.4393461402
Kurtosis-0.2359068916
Mean9.640223112
Median Absolute Deviation (MAD)2.97
Skewness0.5608899719
Sum4054205.47
Variance17.93856924
MonotonicityNot monotonic
2022-08-11T10:08:28.730479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.08568
 
0.1%
6.09558
 
0.1%
6.11549
 
0.1%
6.06547
 
0.1%
6.04541
 
0.1%
6.13535
 
0.1%
6.17534
 
0.1%
5.91519
 
0.1%
6.29516
 
0.1%
6.18511
 
0.1%
Other values (2473)415173
98.7%
ValueCountFrequency (%)
0.81
 
< 0.1%
0.812
 
< 0.1%
0.822
 
< 0.1%
0.835
< 0.1%
0.847
< 0.1%
0.857
< 0.1%
0.865
< 0.1%
0.871
 
< 0.1%
0.882
 
< 0.1%
0.891
 
< 0.1%
ValueCountFrequency (%)
28.821
< 0.1%
28.761
< 0.1%
28.741
< 0.1%
28.731
< 0.1%
28.531
< 0.1%
28.521
< 0.1%
28.391
< 0.1%
28.341
< 0.1%
281
< 0.1%
27.621
< 0.1%

rho (g/m**3)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22972
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1216.062748
Minimum1059.45
Maximum1393.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:28.895163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1059.45
5-th percentile1154.78
Q11187.49
median1213.79
Q31242.77
95-th percentile1282.33
Maximum1393.54
Range334.09
Interquartile range (IQR)55.28

Descriptive statistics

Standard deviation39.97520828
Coefficient of variation (CV)0.03287265262
Kurtosis0.1374465815
Mean1216.062748
Median Absolute Deviation (MAD)27.53
Skewness0.3127056968
Sum511416404.7
Variance1598.017277
MonotonicityNot monotonic
2022-08-11T10:08:29.045023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1222.8969
 
< 0.1%
1184.9164
 
< 0.1%
1199.1563
 
< 0.1%
1205.263
 
< 0.1%
1204.8561
 
< 0.1%
1203.8161
 
< 0.1%
1197.7960
 
< 0.1%
1193.0960
 
< 0.1%
1223.5659
 
< 0.1%
1195.7759
 
< 0.1%
Other values (22962)419932
99.9%
ValueCountFrequency (%)
1059.451
< 0.1%
1059.511
< 0.1%
1063.571
< 0.1%
1064.261
< 0.1%
1064.651
< 0.1%
1066.191
< 0.1%
1100.151
< 0.1%
1100.381
< 0.1%
1101.071
< 0.1%
1101.391
< 0.1%
ValueCountFrequency (%)
1393.541
< 0.1%
1393.261
< 0.1%
1392.561
< 0.1%
1392.291
< 0.1%
1392.11
< 0.1%
1391.881
< 0.1%
1391.821
< 0.1%
1391.631
< 0.1%
1391.621
< 0.1%
1391.62
< 0.1%

wv (m/s)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1193
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.702223844
Minimum-9999
Maximum28.49
Zeros484
Zeros (%)0.1%
Negative18
Negative (%)< 0.1%
Memory size3.2 MiB
2022-08-11T10:08:29.209275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.41
Q10.99
median1.76
Q32.86
95-th percentile5.18
Maximum28.49
Range10027.49
Interquartile range (IQR)1.87

Descriptive statistics

Standard deviation65.44671381
Coefficient of variation (CV)38.44777173
Kurtosis23333.28226
Mean1.702223844
Median Absolute Deviation (MAD)0.88
Skewness-152.7160913
Sum715871.94
Variance4283.272348
MonotonicityNot monotonic
2022-08-11T10:08:29.489899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.981631
 
0.4%
0.861619
 
0.4%
0.921616
 
0.4%
1.081605
 
0.4%
0.911588
 
0.4%
0.951575
 
0.4%
0.791564
 
0.4%
0.841563
 
0.4%
1.061560
 
0.4%
0.721559
 
0.4%
Other values (1183)404671
96.2%
ValueCountFrequency (%)
-999918
 
< 0.1%
0484
0.1%
0.0169
 
< 0.1%
0.0258
 
< 0.1%
0.0371
 
< 0.1%
0.0474
 
< 0.1%
0.0563
 
< 0.1%
0.0656
 
< 0.1%
0.0768
 
< 0.1%
0.0855
 
< 0.1%
ValueCountFrequency (%)
28.491
< 0.1%
14.631
< 0.1%
14.091
< 0.1%
14.011
< 0.1%
13.951
< 0.1%
13.641
< 0.1%
13.591
< 0.1%
13.51
< 0.1%
13.191
< 0.1%
13.091
< 0.1%

max. wv (m/s)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1503
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.056555257
Minimum-9999
Maximum23.5
Zeros435
Zeros (%)0.1%
Negative20
Negative (%)< 0.1%
Memory size3.2 MiB
2022-08-11T10:08:29.648682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.84
Q11.76
median2.96
Q34.74
95-th percentile8.09
Maximum23.5
Range10022.5
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation69.01693185
Coefficient of variation (CV)22.57997191
Kurtosis20974.47356
Mean3.056555257
Median Absolute Deviation (MAD)1.38
Skewness-144.7488651
Sum1285437.37
Variance4763.336882
MonotonicityNot monotonic
2022-08-11T10:08:29.802178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.884273
 
1.0%
23975
 
0.9%
1.643371
 
0.8%
1.763347
 
0.8%
2.883308
 
0.8%
1.523287
 
0.8%
0.883285
 
0.8%
1.683243
 
0.8%
1.363240
 
0.8%
1.923240
 
0.8%
Other values (1493)385982
91.8%
ValueCountFrequency (%)
-999920
 
< 0.1%
0435
0.1%
0.1383
 
< 0.1%
0.24
 
< 0.1%
0.221
 
< 0.1%
0.2421
 
< 0.1%
0.25127
 
< 0.1%
0.2610
 
< 0.1%
0.2850
 
< 0.1%
0.317
 
< 0.1%
ValueCountFrequency (%)
23.51
< 0.1%
22.861
< 0.1%
22.261
< 0.1%
21.581
< 0.1%
20.991
< 0.1%
20.781
< 0.1%
20.691
< 0.1%
20.581
< 0.1%
20.491
< 0.1%
20.41
< 0.1%

wind_angle
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9893
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.7437384
Minimum0
Maximum360
Zeros436
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2022-08-11T10:08:29.957807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.39
Q1124.9
median198.1
Q3234.1
95-th percentile289.9
Maximum360
Range360
Interquartile range (IQR)109.2

Descriptive statistics

Standard deviation86.68169275
Coefficient of variation (CV)0.4960503509
Kurtosis-0.6274652841
Mean174.7437384
Median Absolute Deviation (MAD)46.5
Skewness-0.4920214919
Sum73488653.92
Variance7513.715857
MonotonicityNot monotonic
2022-08-11T10:08:30.112640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.2471
 
0.1%
209.3457
 
0.1%
209.7450
 
0.1%
208.7450
 
0.1%
212448
 
0.1%
210.4446
 
0.1%
211.8444
 
0.1%
210.9443
 
0.1%
211.5443
 
0.1%
211.1441
 
0.1%
Other values (9883)416058
98.9%
ValueCountFrequency (%)
0436
0.1%
0.013
 
< 0.1%
0.028
 
< 0.1%
0.037
 
< 0.1%
0.046
 
< 0.1%
0.059
 
< 0.1%
0.064
 
< 0.1%
0.075
 
< 0.1%
0.086
 
< 0.1%
0.092
 
< 0.1%
ValueCountFrequency (%)
36024
 
< 0.1%
359.946
< 0.1%
359.871
< 0.1%
359.749
< 0.1%
359.658
< 0.1%
359.544
< 0.1%
359.459
< 0.1%
359.346
< 0.1%
359.256
< 0.1%
359.152
< 0.1%

wind_dir
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
SW
130234 
S
89819 
E
79547 
W
56727 
NE
28224 
Other values (2)
36000 

Length

Max length2
Median length1
Mean length1.462388628
Min length1

Characters and Unicode

Total characters615009
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowW
4th rowSW
5th rowSW

Common Values

ValueCountFrequency (%)
SW130234
31.0%
S89819
21.4%
E79547
18.9%
W56727
13.5%
NE28224
 
6.7%
SE19597
 
4.7%
NW16403
 
3.9%

Length

2022-08-11T10:08:30.258115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T10:08:30.449721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sw130234
31.0%
s89819
21.4%
e79547
18.9%
w56727
13.5%
ne28224
 
6.7%
se19597
 
4.7%
nw16403
 
3.9%

Most occurring characters

ValueCountFrequency (%)
S239650
39.0%
W203364
33.1%
E127368
20.7%
N44627
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter615009
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S239650
39.0%
W203364
33.1%
E127368
20.7%
N44627
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin615009
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S239650
39.0%
W203364
33.1%
E127368
20.7%
N44627
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII615009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S239650
39.0%
W203364
33.1%
E127368
20.7%
N44627
 
7.3%

h_sat
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
0
321094 
1
99457 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters420551
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Length

2022-08-11T10:08:30.584182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T10:08:30.703594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number420551
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common420551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII420551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0321094
76.4%
199457
 
23.6%

Interactions

2022-08-11T10:08:19.592484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:34.659781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:38.726115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:41.976174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:45.294448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:48.458516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:52.641400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:56.338832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:00.156181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:03.440983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:06.727956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:10.083117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:13.447352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:16.625578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:19.807237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:34.969473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:38.993030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:42.181209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:45.508917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:48.686700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:52.917180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:56.722103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:00.392970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:03.669990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:06.964366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:10.324679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:13.662915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:16.830434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:20.026383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:35.207571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:39.221943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:42.410930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:45.718027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:49.127596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:53.190144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:56.969174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:00.651965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:03.897536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:07.187641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:10.605239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:13.887855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:17.050207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:20.249572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:35.443501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:39.439496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:42.759636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:45.937807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:49.531348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:53.439962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:57.215786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:00.907834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:04.129403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:07.425728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:10.859524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:14.108515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:17.266583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:20.466955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:35.716309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:39.658101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:42.970188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:46.141215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:49.800658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:53.744815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:57.483710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:01.154961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:04.341160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:07.676399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:11.121078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:14.317814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:17.474113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:20.680550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-11T10:07:39.886885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:43.169872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-11T10:08:14.525953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-11T10:07:43.382740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:46.572631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:50.382857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:54.213594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:57.992396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:01.587323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:04.801171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:08.165897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:11.630849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:14.743121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:17.889700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:21.112429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:36.603953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:40.370360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:43.588294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:46.813533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:50.674297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:54.436937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:58.222353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:01.794122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:05.035643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:08.391133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:11.875658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:14.958454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:18.109162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:21.452241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:36.830994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:40.586785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:43.793135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:47.012995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:51.046438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:54.651954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:58.456595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:02.009495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:05.267001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:08.604483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:12.090221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:15.175957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:18.310656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:21.668822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:37.432249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:40.843712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:44.016477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:47.270361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:51.327986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:54.922333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:58.695944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:02.225387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:05.559711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:08.821605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:12.322386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:15.404669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:18.524584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:21.892547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:37.750119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:41.086225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:44.266473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:47.512040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:51.574251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:55.189555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:58.914462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:02.433444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:05.797330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:09.046418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:12.540359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:15.773246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:18.747962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:22.112610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:37.981151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:41.319039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:44.530771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:47.738501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:51.839304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:55.445788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:59.201225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:02.654726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:06.031701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:09.275585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:12.779374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:15.996851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:18.958630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:22.337937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:38.240374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:41.547730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:44.784086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:48.017380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:52.114022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:55.670063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:59.480701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:02.856271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:06.287245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:09.630652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:12.991392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:16.204805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:19.161116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:22.542070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:38.486134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:41.761364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:45.076653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:48.234293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:52.390048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:56.010461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:07:59.820836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:03.075782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:06.509873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:09.838963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:13.218088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:16.414015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T10:08:19.373977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-11T10:08:30.811961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-11T10:08:31.047864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-11T10:08:31.272003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-11T10:08:31.480863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-11T10:08:31.626076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-11T10:08:22.896843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-11T10:08:23.787258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wind_anglewind_dirh_sat
001.01.2009 00:10:00996.52-8.02265.40-8.9093.33.333.110.221.943.121307.751.031.75152.3W1
101.01.2009 00:20:00996.57-8.41265.01-9.2893.43.233.020.211.893.031309.800.721.50136.1W1
201.01.2009 00:30:00996.53-8.51264.91-9.3193.93.213.010.201.883.021310.240.190.63171.6W1
301.01.2009 00:40:00996.51-8.31265.12-9.0794.23.263.070.191.923.081309.190.340.50198.0SW1
401.01.2009 00:50:00996.51-8.27265.15-9.0494.13.273.080.191.923.091309.000.320.63214.3SW1
501.01.2009 01:00:00996.50-8.05265.38-8.7894.43.333.140.191.963.151307.860.210.63192.7SW1
601.01.2009 01:10:00996.50-7.62265.81-8.3094.83.443.260.182.043.271305.680.180.63166.5W1
701.01.2009 01:20:00996.50-7.62265.81-8.3694.43.443.250.192.033.261305.690.190.50118.6NW1
801.01.2009 01:30:00996.50-7.91265.52-8.7393.83.363.150.211.973.161307.170.280.75188.5SW1
901.01.2009 01:40:00996.53-8.43264.99-9.3493.13.233.000.221.883.021309.850.590.88185.0SW1

Last rows

Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wind_anglewind_dirh_sat
42054131.12.2016 22:30:001000.44-4.08269.05-7.8974.604.513.371.152.103.371293.551.272.48192.1SW0
42054231.12.2016 22:40:001000.45-4.45268.68-7.1581.304.393.570.822.223.571295.240.801.44183.8SW0
42054331.12.2016 22:50:001000.32-4.09269.05-7.2378.604.513.540.962.213.541293.371.251.60199.2SW0
42054431.12.2016 23:00:001000.21-3.76269.39-7.9572.504.623.351.272.093.351291.710.891.30223.7SW0
42054531.12.2016 23:10:001000.11-3.93269.23-8.0972.604.563.311.252.063.311292.410.561.00202.6SW0
42054631.12.2016 23:20:001000.07-4.05269.10-8.1373.104.523.301.222.063.301292.980.671.52240.0S0
42054731.12.2016 23:30:00999.93-3.35269.81-8.0669.714.773.321.442.073.321289.441.141.92234.3S0
42054831.12.2016 23:40:00999.82-3.16270.01-8.2167.914.843.281.552.053.281288.391.082.00215.2SW0
42054931.12.2016 23:50:00999.81-4.23268.94-8.5371.804.463.201.261.993.201293.561.492.16225.8S0
42055001.01.2017 00:00:00999.82-4.82268.36-8.4275.704.273.231.042.013.231296.381.231.96184.9SW0

Duplicate rows

Most frequently occurring

Date Timep (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wind_anglewind_dirh_sat# duplicates
001.07.2010 00:10:00992.0617.87291.6914.0678.420.5016.074.4310.1416.201180.210.310.5651.11NE02
101.07.2010 00:20:00992.0217.82291.6514.0378.520.4416.044.3910.1216.171180.380.230.4852.64NE02
201.07.2010 00:30:00992.0417.92291.7514.0978.320.5716.104.4610.1616.231179.970.180.4022.10E02
301.07.2010 00:40:00991.9617.82291.6514.0278.420.4416.024.4110.1116.151180.320.190.40354.80E02
401.07.2010 00:50:00991.9017.54291.3813.9679.520.0815.964.1210.0716.101181.410.240.9821.40E02
501.07.2010 01:00:00991.8117.35291.1913.8780.019.8415.873.9710.0216.001182.120.440.9649.64NE02
601.07.2010 01:10:00991.8117.11290.9513.8381.019.5415.833.719.9915.961183.110.340.76296.10SE02
701.07.2010 01:20:00991.8516.90290.7413.7481.619.2815.743.559.9315.871184.061.211.76239.50S02
801.07.2010 01:30:00991.8216.87290.7113.6181.119.2515.613.649.8515.741184.211.752.28222.20SW02
901.07.2010 01:40:00991.8116.69290.5313.5981.919.0315.583.449.8315.711184.941.041.64209.90SW02